import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]							)   ->    Any:
		_lowerCamelCase				   =  VideoMAEConfig()

		set_architecture_configs(UpperCamelCase			,       UpperCamelCase							)

		if "finetuned" not in model_name:
				_lowerCamelCase				   =  False

		if "finetuned" in model_name:
				_lowerCamelCase				   =  'huggingface/label-files'
				if "kinetics" in model_name:
						_lowerCamelCase				   =  4_00
						_lowerCamelCase				   =  'kinetics400-id2label.json'
				elif "ssv2" in model_name:
						_lowerCamelCase				   =  1_74
						_lowerCamelCase				   =  'something-something-v2-id2label.json'
				else:
						raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'							)
				_lowerCamelCase				   =  json.load(open(hf_hub_download(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)			,       'r'							)							)
				_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}
				_lowerCamelCase				   =  idalabel
				_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		return config


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]			,       UpperCamelCase     :				List[str]							)   ->    List[Any]:
		if "small" in model_name:
				_lowerCamelCase				   =  3_84
				_lowerCamelCase				   =  15_36
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  16
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  3
				_lowerCamelCase				   =  1_92
				_lowerCamelCase				   =  7_68
		elif "large" in model_name:
				_lowerCamelCase				   =  10_24
				_lowerCamelCase				   =  40_96
				_lowerCamelCase				   =  24
				_lowerCamelCase				   =  16
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  8
				_lowerCamelCase				   =  5_12
				_lowerCamelCase				   =  20_48
		elif "huge" in model_name:
				_lowerCamelCase				   =  12_80
				_lowerCamelCase				   =  51_20
				_lowerCamelCase				   =  32
				_lowerCamelCase				   =  16
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  8
				_lowerCamelCase				   =  6_40
				_lowerCamelCase				   =  25_60
		elif "base" not in model_name:
				raise ValueError('Model name should include either "small", "base", "large", or "huge"'							)


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]							)   ->    Any:
		if "encoder." in name:
				_lowerCamelCase				   =  name.replace('encoder.'			,       ''							)
		if "cls_token" in name:
				_lowerCamelCase				   =  name.replace('cls_token'			,       'videomae.embeddings.cls_token'							)
		if "decoder_pos_embed" in name:
				_lowerCamelCase				   =  name.replace('decoder_pos_embed'			,       'decoder.decoder_pos_embed'							)
		if "pos_embed" in name and "decoder" not in name:
				_lowerCamelCase				   =  name.replace('pos_embed'			,       'videomae.embeddings.position_embeddings'							)
		if "patch_embed.proj" in name:
				_lowerCamelCase				   =  name.replace('patch_embed.proj'			,       'videomae.embeddings.patch_embeddings.projection'							)
		if "patch_embed.norm" in name:
				_lowerCamelCase				   =  name.replace('patch_embed.norm'			,       'videomae.embeddings.norm'							)
		if "decoder.blocks" in name:
				_lowerCamelCase				   =  name.replace('decoder.blocks'			,       'decoder.decoder_layers'							)
		if "blocks" in name:
				_lowerCamelCase				   =  name.replace('blocks'			,       'videomae.encoder.layer'							)
		if "attn.proj" in name:
				_lowerCamelCase				   =  name.replace('attn.proj'			,       'attention.output.dense'							)
		if "attn" in name and "bias" not in name:
				_lowerCamelCase				   =  name.replace('attn'			,       'attention.self'							)
		if "attn" in name:
				_lowerCamelCase				   =  name.replace('attn'			,       'attention.attention'							)
		if "norm1" in name:
				_lowerCamelCase				   =  name.replace('norm1'			,       'layernorm_before'							)
		if "norm2" in name:
				_lowerCamelCase				   =  name.replace('norm2'			,       'layernorm_after'							)
		if "mlp.fc1" in name:
				_lowerCamelCase				   =  name.replace('mlp.fc1'			,       'intermediate.dense'							)
		if "mlp.fc2" in name:
				_lowerCamelCase				   =  name.replace('mlp.fc2'			,       'output.dense'							)
		if "decoder_embed" in name:
				_lowerCamelCase				   =  name.replace('decoder_embed'			,       'decoder.decoder_embed'							)
		if "decoder_norm" in name:
				_lowerCamelCase				   =  name.replace('decoder_norm'			,       'decoder.decoder_norm'							)
		if "decoder_pred" in name:
				_lowerCamelCase				   =  name.replace('decoder_pred'			,       'decoder.decoder_pred'							)
		if "norm.weight" in name and "decoder" not in name and "fc" not in name:
				_lowerCamelCase				   =  name.replace('norm.weight'			,       'videomae.layernorm.weight'							)
		if "norm.bias" in name and "decoder" not in name and "fc" not in name:
				_lowerCamelCase				   =  name.replace('norm.bias'			,       'videomae.layernorm.bias'							)
		if "head" in name and "decoder" not in name:
				_lowerCamelCase				   =  name.replace('head'			,       'classifier'							)

		return name


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    Optional[Any]:
		for key in orig_state_dict.copy().keys():
				_lowerCamelCase				   =  orig_state_dict.pop(UpperCamelCase							)

				if key.startswith('encoder.'							):
						_lowerCamelCase				   =  key.replace('encoder.'			,       ''							)

				if "qkv" in key:
						_lowerCamelCase				   =  key.split('.'							)
						if key.startswith('decoder.blocks'							):
								_lowerCamelCase				   =  config.decoder_hidden_size
								_lowerCamelCase				   =  int(key_split[2]							)
								_lowerCamelCase				   =  'decoder.decoder_layers.'
								if "weight" in key:
										_lowerCamelCase				   =  val[:dim, :]
										_lowerCamelCase				   =  val[dim : dim * 2, :]
										_lowerCamelCase				   =  val[-dim:, :]
						else:
								_lowerCamelCase				   =  config.hidden_size
								_lowerCamelCase				   =  int(key_split[1]							)
								_lowerCamelCase				   =  'videomae.encoder.layer.'
								if "weight" in key:
										_lowerCamelCase				   =  val[:dim, :]
										_lowerCamelCase				   =  val[dim : dim * 2, :]
										_lowerCamelCase				   =  val[-dim:, :]
				else:
						_lowerCamelCase				   =  val

		return orig_state_dict


def 	lowerCamelCase					(	)   ->    str:
		_lowerCamelCase				   =  hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'			,       filename='eating_spaghetti.npy'			,       repo_type='dataset'							)
		_lowerCamelCase				   =  np.load(UpperCamelCase							)
		return list(UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				Any			,       UpperCamelCase     :				Optional[Any]			,       UpperCamelCase     :				str							)   ->    Optional[int]:
		_lowerCamelCase				   =  get_videomae_config(UpperCamelCase							)

		if "finetuned" in model_name:
				_lowerCamelCase				   =  VideoMAEForVideoClassification(UpperCamelCase							)
		else:
				_lowerCamelCase				   =  VideoMAEForPreTraining(UpperCamelCase							)

		# download original checkpoint, hosted on Google Drive
		_lowerCamelCase				   =  'pytorch_model.bin'
		gdown.cached_download(UpperCamelCase			,       UpperCamelCase			,       quiet=UpperCamelCase							)
		_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location='cpu'							)
		if "model" in files:
				_lowerCamelCase				   =  files['model']
		else:
				_lowerCamelCase				   =  files['module']
		_lowerCamelCase				   =  convert_state_dict(UpperCamelCase			,       UpperCamelCase							)

		model.load_state_dict(UpperCamelCase							)
		model.eval()

		# verify model on basic input
		_lowerCamelCase				   =  VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,       image_std=[0.5, 0.5, 0.5]							)
		_lowerCamelCase				   =  prepare_video()
		_lowerCamelCase				   =  image_processor(UpperCamelCase			,       return_tensors='pt'							)

		if "finetuned" not in model_name:
				_lowerCamelCase				   =  hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'			,       filename='bool_masked_pos.pt'							)
				_lowerCamelCase				   =  torch.load(UpperCamelCase							)

		_lowerCamelCase				   =  model(**UpperCamelCase							)
		_lowerCamelCase				   =  outputs.logits

		_lowerCamelCase				   =  [
		    'videomae-small-finetuned-kinetics',
		    'videomae-small-finetuned-ssv2',
		    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
		    'videomae-base-short',
		    'videomae-base-short-finetuned-kinetics',
		    'videomae-base',
		    'videomae-base-finetuned-kinetics',
		    'videomae-large',
		    'videomae-large-finetuned-kinetics',
		    'videomae-huge-finetuned-kinetics',
		    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
		    'videomae-base-short-ssv2',
		    'videomae-base-short-finetuned-ssv2',
		    'videomae-base-ssv2',
		    'videomae-base-finetuned-ssv2',
		]

		# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
		if model_name == "videomae-small-finetuned-kinetics":
				_lowerCamelCase				   =  torch.Size([1, 4_00]							)
				_lowerCamelCase				   =  torch.tensor([-0.9_291, -0.4_061, -0.9_307]							)
		elif model_name == "videomae-small-finetuned-ssv2":
				_lowerCamelCase				   =  torch.Size([1, 1_74]							)
				_lowerCamelCase				   =  torch.tensor([0.2_671, -0.4_689, -0.8_235]							)
		elif model_name == "videomae-base":
				_lowerCamelCase				   =  torch.Size([1, 14_08, 15_36]							)
				_lowerCamelCase				   =  torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]							)
		elif model_name == "videomae-base-short":
				_lowerCamelCase				   =  torch.Size([1, 14_08, 15_36]							)
				_lowerCamelCase				   =  torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]							)
				# we verified the loss both for normalized and unnormalized targets for this one
				_lowerCamelCase				   =  torch.tensor([0.5_142]							) if config.norm_pix_loss else torch.tensor([0.6_469]							)
		elif model_name == "videomae-large":
				_lowerCamelCase				   =  torch.Size([1, 14_08, 15_36]							)
				_lowerCamelCase				   =  torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]							)
		elif model_name == "videomae-large-finetuned-kinetics":
				_lowerCamelCase				   =  torch.Size([1, 4_00]							)
				_lowerCamelCase				   =  torch.tensor([0.0_771, 0.0_011, -0.3_625]							)
		elif model_name == "videomae-huge-finetuned-kinetics":
				_lowerCamelCase				   =  torch.Size([1, 4_00]							)
				_lowerCamelCase				   =  torch.tensor([0.2_433, 0.1_632, -0.4_894]							)
		elif model_name == "videomae-base-short-finetuned-kinetics":
				_lowerCamelCase				   =  torch.Size([1, 4_00]							)
				_lowerCamelCase				   =  torch.tensor([0.6_588, 0.0_990, -0.2_493]							)
		elif model_name == "videomae-base-finetuned-kinetics":
				_lowerCamelCase				   =  torch.Size([1, 4_00]							)
				_lowerCamelCase				   =  torch.tensor([0.3_669, -0.0_688, -0.2_421]							)
		elif model_name == "videomae-base-short-ssv2":
				_lowerCamelCase				   =  torch.Size([1, 14_08, 15_36]							)
				_lowerCamelCase				   =  torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]							)
		elif model_name == "videomae-base-short-finetuned-ssv2":
				_lowerCamelCase				   =  torch.Size([1, 1_74]							)
				_lowerCamelCase				   =  torch.tensor([-0.0_537, -0.1_539, -0.3_266]							)
		elif model_name == "videomae-base-ssv2":
				_lowerCamelCase				   =  torch.Size([1, 14_08, 15_36]							)
				_lowerCamelCase				   =  torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]							)
		elif model_name == "videomae-base-finetuned-ssv2":
				_lowerCamelCase				   =  torch.Size([1, 1_74]							)
				_lowerCamelCase				   =  torch.tensor([0.1_961, -0.8_337, -0.6_389]							)
		else:
				raise ValueError(F"""Model name not supported. Should be one of {model_names}"""							)

		# verify logits
		assert logits.shape == expected_shape
		if "finetuned" in model_name:
				assert torch.allclose(logits[0, :3]			,       UpperCamelCase			,       atol=1e-4							)
		else:
				print('Logits:'			,       logits[0, :3, :3]							)
				assert torch.allclose(logits[0, :3, :3]			,       UpperCamelCase			,       atol=1e-4							)
		print('Logits ok!'							)

		# verify loss, if applicable
		if model_name == "videomae-base-short":
				_lowerCamelCase				   =  outputs.loss
				assert torch.allclose(UpperCamelCase			,       UpperCamelCase			,       atol=1e-4							)
				print('Loss ok!'							)

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""							)
				image_processor.save_pretrained(UpperCamelCase							)
				model.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				print('Pushing to the hub...'							)
				model.push_to_hub(UpperCamelCase			,       organization='nielsr'							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_url',
				    default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
				    type=str,
				    help=(
				        'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
				        ' download link.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='/Users/nielsrogge/Documents/VideoMAE/Test',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				A						=			parser.parse_args()
				convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()